Empowering trusted intermediaries to navigate the complex challenges of COVID-19 vaccination in ethnocultural communities
Bibliographic record
Abstract
Context: Cultural health brokers are intermediaries between community and formal systems bridging cultural, linguistic, and knowledge gaps. Objective: Understanding how brokers addressed the evolving complexity of COVID-19 vaccination in their communities through sensemaking, a continuous process to establish situational awareness to support understanding and action. Study Design and Analysis: We partnered with twenty-eight brokers capturing their self-reflections and our weekly group 90 minute discussions from Sept. 16 to Dec.16, 2021 as they navigated COVID-19 vaccination controversies in their communities. Reflections were captured in the SenseMaker platform, a mixed-methods data collection tool and the weekly sessions were recorded, transcribed and managed in NVivo. Inductive and deductive coding, iterative triangulation with the Broker reflections and our analytical and reflexive thinking constructed themes. Setting/Population: The multicultural health brokers co-operative of community cultural health brokers with immigrant and refugee lived experience, Edmonton, Canada serving 10 000 people from diverse ethnolinguistic communities. Intervention: Real time outreach, information sharing, resource creation and pop-up clinics. Outcome measures: 277 Real-time narrative data collection and self-interpretation in the Sensemaker platform, a mixed-method data collection tool. Transcripts of five final sessions focused on synthesis of learnings. Results: Intermediaries work in contextually and culturally appropriate ways, leveraging trust with diverse fields they bridge, and mobilizing action by exaptation from previous experience in crisis navigation. There were four entwined components to navigation of the evolving complexity of COVID-19 vaccination: trust, relationships, creation of safe spaces for collective sensemaking and solution finding, and leveraging cultural and social capital to address challenges and barriers to meeting peoples’ needs. Brokers worked to reduce decisional conflict and misinformation to support people making informed, values-congruent decisions. Conclusions: Supporting trusted intermediaries with existing relationships, solutions, and infrastructure will advance ongoing pandemic response and recovery efforts, and future emergency planning to strengthen the resilience of health systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".